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The Mechanism Research of Green Supply Chain Synergy Profit Distribution Based on the Shapley Value Method
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作者 CHU Liqin LI Bo 《International English Education Research》 2016年第12期15-17,共3页
Under green supply chain mode, how to Carry out the distribution of profits between subjects is an important problem. Through the comparison of the green supply chain benefit allocation of non-cooperative game and coo... Under green supply chain mode, how to Carry out the distribution of profits between subjects is an important problem. Through the comparison of the green supply chain benefit allocation of non-cooperative game and cooperative game the payoffmatrix, it is clearly that the necessity of interest distribution cooperative game. Put general manufacturing enterprises of green supply chain as the research object, using Shapley value method for theory analysis and example verification, vertifys that enterprise synergy gains more than their own separate management, and puts forward a feasible path of supply chain collaboration through the construction of the distribution of interests coordination model. 展开更多
关键词 Green supply chain profit distribution synergy Shapley value mode
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Randomized Objective Function Linear Programming in Risk Management
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作者 Dennis Ridley Felipe Llaugel +1 位作者 Inger Daniels Abdullah Khan 《Journal of Applied Mathematics and Physics》 2021年第3期391-402,共12页
The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex method, one consid... The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each objective function coefficient, one at a time. This yields the range of optimality within which the decision variables remain constant. This sensitivity analysis is useful for helping the analyst get a sense for the problem. However, it is unrealistic because objective function coefficients tend not to stand still. They are typically profit contributions from products sold and are subject to randomly varying selling prices. In this paper, a realistic linear program is created for simultaneously randomizing the coefficients from any probability distribution. Furthermore, we present a novel approach for designing a copula of random objective function coefficients according to a specified rank correlation. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendency, spread, skewness and extreme values for the purpose of risk analysis. This enables risk analysis and business analytics, emerging topics in education and preparation for the knowledge economy. 展开更多
关键词 Linear Programming RANDOM Objective Function profit distribution RISK Monte Carlo Simulation
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Randomized Constraint Limit Linear Programming in Risk Management
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作者 Dennis Ridley Abdullah Khan 《Journal of Applied Mathematics and Physics》 2020年第11期2691-2702,共12页
Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex m... Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each constraint limit, one at a time. This yields the range of feasibility within which the solution remains feasible. This sensitivity analysis is useful for helping the analyst get a feel for the problem. However, it is unrealistic because some constraint limits can vary randomly. These are typically constraint limits based on expected inventory. Inventory may fall short if there are overdue deliveries, unplanned machine failure, spoilage, etc. A realistic LP is created for simultaneously randomizing the constraint limits from any probability distribution. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendencies, spread, skewness and extreme values for the purpose of risk analysis. The spreadsheet design presented is ideal for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics with no specialized programming language requirement. 展开更多
关键词 Pedagogic Effectiveness of Big Data Analytics Linear Programming Stochastic Optimization Constraint Limit profit distribution and Risk Monte Carlo Simulation
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